Estimating evapotranspiration in hilly watersheds is paramount for managing water resources, especially in semiarid/subhumid regions. The eddy covariance (EC) technique allows continuous measurements of latent heat flux (LE). However, time series of EC measurements often experience large portions of missing data because of instrumental malfunctions or quality filtering. Existing gap-filling methods are questionable over hilly crop fields because of changes in airflow inclination and subsequent aerodynamic properties. We evaluated the performances of different gap-filling methods before and after tailoring to conditions of hilly crop fields. The tailoring consisted of splitting the LE time series beforehand on the basis of upslope and downslope winds. The experiment was setup within an agricultural hilly watershed in northeastern Tunisia. EC measurements were collected throughout the growth cycle of three wheat crops, two of them located in adjacent fields on opposite hillslopes, and the third one located in a flat field. We considered four gap-filling methods: the REddyProc method, the linear regression between LE and net radiation (Rn), the multi-linear regression of LE against the other energy fluxes, and the use of evaporative fraction (EF). Regardless of the method, the splitting of the LE time series did not impact the gap-filling rate, and it might improve the accuracies on LE retrievals in some cases. Regardless of the method, the obtained accuracies on LE estimates after gap filling were close to instrumental accuracies, and they were comparable to those reported in previous studies over flat and mountainous terrains. Overall, REddyProc was the most appropriate method, for both gap-filling rate and retrieval accuracy. Thus, it seems possible to conduct gap filling for LE time series collected over hilly crop fields, provided the LE time series are split beforehand on the basis of upslope–downslope winds. Future works should address consecutive vegetation growth cycles for a larger panel of conditions in terms of climate, vegetation, and water status.

There is a strong need for long term observations of land surface fluxes such as actual evapotranspiration (ETa). Eddy covariance (EC) method is widely used to provide ETa measurements, and several gap-filling methods have been proposed to complete inherent missing data. However, implementing gap-filling methods is questionable for EC time series collected within hilly agricultural areas at the watershed extent. Indeed, changes in wind direction induce changes in airflow inclination and footprint, and therefore possibly induce changes in the relationships on which rely gap-filling methods. This study aimed to obtain continuous ETa time series by adapting gap-filling methods to the particular conditions abovementioned. The experiment took place within an agricultural watershed in north-eastern Tunisia. A 9.6-m-high EC flux tower has been operating close to the watershed center since 2010. The sensible and latent heat fluxes data collected from 2010 to 2013 were quality controlled, and the REddyProc software was used to fill gaps at the hourly timescale. Adapting REddyProc method consisted of splitting the dataset according to wind direction, which improved the flux data at the hourly timescale, but not at the daily and monthly timescales. Finally, complete time series permitted to analyze seasonal and inter-annual variability of ETa.

As agricultural hilly watersheds are widespread throughout the world, there is a strong need for reliable estimates of land surface fluxes, especially evapotranspiration, over crop fields on hilly slopes. In order to obtain reliable estimates from eddy covariance (EC) measurements in such conditions, the current study aimed at proposing adequate planar fit tilt corrections that account for the combined effects of topography, wind direction, and vegetation height on airflow inclinations. EC measurements were collected within an agricultural hilly watershed in northeastern Tunisia, throughout the growth cycles of cereals, legumes, and pasture. The wind had two dominant directions that induced upslope and downslope winds. For upslope winds, the airflows were parallel to the slopes and slightly came closer to the horizontal plane when vegetation grew. For downslope winds, over fields located in the lee of the rim top, the airflows were almost horizontal over bare soil and came closer to the topographical slope when vegetation grew. We therefore adjusted the planar fit tilt correction on EC measurements according to vegetation height and by discriminating between upslope and downslope winds. This adjusted tilt correction improved the energy balance closure in most cases, and the obtained energy balance closures were similar to that reported in the literature for flat conditions. We conclude that EC data collected within crop fields on hilly slopes can be used for monitoring land surface fluxes, provided planar fit tilt corrections are applied in an appropriate manner.

Understanding the temporal and spatial variability of soil moisture is fundamentalfor improving the management of water and soil resources on rainfed agrosystems.The present study focused on the soil moisture (SM) patterns observed in a hillyrainfed agrosystem. We analyzed five datasets from measurement at 15 sites duringvarious crop growth cycles under the common cereals/legumes/pasture croppingsystems within the Kamech catchment in Tunisia. Results indicated a strongseasonality in the precipitation and evapotranspiration dynamics that stronglyinfluences soil moisture patterns, with a strong reduction in the water availabilityduring summer (average SM = 0.20 m3/m3) as compared to winter (average SM =0.40 m3/m3). The data of two consecutive years showed that the spatial variability,expressed through the coefficient of variation of soil moisture, was at its highestpoint during spring. The averaged soil moisture of the two years yielded areasonable significant linear relation (R²=0.67**), indicating temporal stability ofthe spatial pattern. Actual evapotranspiration was found to be the predominantfactor influencing the dynamics of soil moisture.

In the context of a changing climate, monitoring agricultural systems is becoming increasingly important. Remote sensing products provide essential information for the crop classification application, which is used to produce thematic maps. High-resolution and regional-scale maps of agricultural land are required to develop better adapted future strategies. Nevertheless, the performance of crop classification using large spatio-temporal data remains challenging due to the difficulties in handling huge amounts of input data (different spatial and temporal resolutions). This paper proposes an innovative approach of remote sensing data management that was used to prepare the input data for the crop classification application. This classification was carried out in the Cap Bon region, Tunisia, to classify citrus groves among two other crop classes (olive groves and open field) using multi-temporal remote sensing data from Sentinel- 1 and Sentinel-2 satellite platforms. Thus, we described the new QGIS plugin “Model Management Tool (MMT)”. This plugin was designed to manage large Earth observation (EO) data. This tool is based on the combination of two concepts: (i) the local nested grid (LNG) called Tuplekeys and (ii) Datacubes. Tuplekeys or special spatial regions were created within a LNG to allow a proper integration between the data of both sensors. The Datacubes concept allows to provide an arranged array of time-series multi-dimensional stacks (space, time and data) of gridded data. Two different classification processes were performed based on the selection of the input feature (the obtained time-series as input data: NDVI and NDVI + VV + VH) and on the most accurate algorithm for each scenario (22 tested classifiers). The obtained results revealed that the best classification performance and highest accuracy were obtained with the scenario using only optical-based information (NDVI), with an overall accuracy OA = 0.76. This result was obtained by support vector machine (SVM). As for the scenario relying on the combination of optical and SAR data (NDVI + VV + VH), it presented an OA = 0.58. Our results demonstrate the usefulness of the new data management tool in organizing the input classification data. Additionally, our results highlight the importance of optical data to provide acceptable classification performance especially for a complex landscape such as that of the Cap Bon. The information obtained from this work will allow the estimation of the water requirements of citrus orchards and the improvement of irrigation scheduling methodologies. Likewise, many future methodologies will certainly rely on the combination of Tuplekeys and Datacubes concepts which have been tested within the MMT tool.